Image registration in general is concerned with determining a precise geometric match between two or more images, of the same object or area, that are from different times or taken from different positions relative to the image content. In the present invention, the primary emphasis is on images (dental radiographs) taken on different dates or times. Comparison of such imagery, after registration, allows detailed analysis of any changes that may have occurred, e.g., new or larger cavities, bone loss, loosened fillings, etc.
Registration can also be applied to two different forms of data such as a map and an image of the same area. The registration process relies on tie points, which are points (image positions) of the same object in different images. Tie points must be accurately placed, and must be unambiguously identified. The tie points are then used to generate a polynomial function that is used to warp one image to another.
Tie point selection can be an arduous process, requiring users to repeatedly cycle between overviews of the imagery and close-up views as they attempt to identify and then precisely indicate the common locations. The process of “zooming-in” and “zooming-out” can be time consuming, as well as disconcerting, frequently resulting in the user losing context, i.e., not being sure of which part of the image is being viewed.
Image registration is an important element in isolating historical changes in film or digital imagery. Change detection, in this context, is an image based concept, and refers to the process of comparing imagery over an area of interest taken at two different times. Images are compared either manually or automatically to determine those places where some change in the scene content has occurred. Imagery based change detection can be performed on a variety of image types, including panchromatic, color, IR and multi-spectral image types. In some applications, the size, location and type of change can be determined.
In aerial applications (such as the Clear Change Imagery Service provided by Eastman Kodak Company), the before and after images are corrected for differences in rotation, scale and alignment. The process is semi-automatic. The user selects 2-3 common points between the images (using a tie point “wizard”), and the system automatically finds the rest. As part of the registration process, the images are automatically clipped to the maximum overlapping region. Change is represented as a color overlay, in which the presence of a particular color (e.g., green) indicates change at that position.
The underlying correlation routines contained in the aforementioned aerial application were developed specifically for the taking conditions, imagery spectrum, and scene content typical to that application. There are a number of reasons why such routines do not readily transfer over to dental applications:
1. The prior art embodied in Clear Change Imagery Service and the Registration Tool (the latter being a subset of the former) was developed specifically for the case of images to be used for aerial mapping. Such imagery is limited to tilt angles of less than 3 degrees relative to vertical through the means of gyroscopically stabilized mounts. Consequently, the imagery exhibits very little aspect change in the imaged objects. As this is the case, the registration algorithms are able to successfully run without any preliminary image rectification to similar look (capture) angles. The dental case under discussion does not include such control of the acquisition process. Dental x-rays have been known to include as much as 15 degrees of tilt from the vertical and horizontal axes. Further, the tilt values are not recorded, nor are they necessarily the same from x-ray to x-ray.
2. The aerial image case generally produces images of high contrast, and with a significant number of well defined features (exceptions include lakes, deserts, etc.). These two factors combine to provide favorable conditions for automated tie point finding routines that generally rely on correlation of image pixel values around a prospective point. Strong correlation is most often found in cases where distinct patterns are present and which differ significantly from nearby points. Thus the match, or correlation, is more likely to be unique. X-ray images are in general low in contrast, and exhibit large areas of relatively amorphous material, e.g., an image of a tooth (vs. the edge of the tooth), or an image of the gingiva which exhibits fine, but relatively unstructured detail.
3. The aerial image case yields images that are reflective in nature and therefore represent a single instance of the outside surface(s) of the imaged objects. X-ray images are the result of a radiation penetrating process and represent the effect of the integrated density of all material encountered on the transmitted path between the x-ray emitter and the detector (sensitized film or electronic).
It is therefore necessary to specifically address the issues of maintaining context and enabling precision placement of tie points, while also providing a very fast and easy to use process in a dental setting. What is accordingly needed is a technique for efficient preliminary image registration of dental radiographs to similar look angles, which can handle radiographic images that are low in contrast and that exhibit an integrated density of material encountered between the x-ray emitter and the detector (sensitized film or electronic).